We investigated the seasonal trends of OA sources affecting the air quality
of Marseille (France), which is the largest harbor of the Mediterranean Sea.
This was achieved by measurements of nebulized filter extracts using an
aerosol mass spectrometer (offline-AMS). In total 216 PM2.5 (particulate
matter with an aerodynamic diameter < 2.5 µm) filter samples
were collected over 1 year from August 2011 to July 2012. These filters were
used to create 54 composite samples which were analyzed by offline-AMS. The
same samples were also analyzed for major water-soluble ions, metals,
elemental and organic carbon (EC / OC), and organic markers, including
n-alkanes, hopanes, polycyclic aromatic hydrocarbons (PAHs), lignin and cellulose
pyrolysis products, and nitrocatechols. The application of positive matrix
factorization (PMF) to the water-soluble AMS spectra enabled the extraction
of five factors, related to hydrocarbon-like OA (HOA), cooking OA (COA),
biomass burning OA (BBOA), oxygenated OA (OOA), and an industry-related OA
(INDOA). Seasonal trends and relative contributions of OA sources were
compared with the source apportionment of OA spectra collected from the AMS
field deployment at the same station but in different years and for shorter
monitoring periods (February 2011 and July 2008). Online- and offline-AMS
source apportionment revealed comparable seasonal contribution of the
different OA sources. Results revealed that BBOA was the dominant source
during winter, representing on average 48 % of the OA, while during summer
the main OA component was OOA (63 % of OA mass on average). HOA related
to traffic emissions contributed on a yearly average 17 % to the OA mass,
while COA was a minor source contributing 4 %. The contribution of INDOA
was enhanced during winter (17 % during winter and 11 % during
summer), consistent with an increased contribution from light alkanes, light
PAHs (fluoranthene, pyrene, phenanthrene), and selenium, which is commonly
considered as a unique coal combustion and coke production marker. Online-
and offline-AMS source apportionments revealed evolving levoglucosan : BBOA
ratios, which were higher during late autumn and March. A similar seasonality was
observed in the ratios of cellulose combustion markers to lignin combustion
markers, highlighting the contribution from cellulose-rich biomass
combustion, possibly related to agricultural activities.

Introduction

Outdoor particulate air pollution is estimated to be responsible for
approximately 3.3 million premature deaths each year worldwide, and this
number is projected to double by 2050 (Lelieveld et al., 2015). Organic
aerosols (OA) can contribute up to 90 % of the PM1 (Jimenez et al.,
2009), and therefore understanding their main emission sources and formation
processes is a key prerequisite for the development of appropriate mitigation
policies.

In the Mediterranean basin, sources and trends of OA remain scarcely
investigated, despite their deleterious impact in such a densely populated
region. The Mediterranean region is characterized by an intense
photochemistry during summer. Not surprisingly, the majority of the OA source
apportionment studies conducted in the region using aerosol mass spectrometry
(AMS, Canagaratna et al., 2007) focused on the summer period (e.g., El Haddad et al., 2013;
Minguillón et al., 2011, 2016; Hildebrandt et al., 2011). Through
positive matrix factorization (PMF) techniques, these studies revealed that
during summer the oxygenated organic aerosol (OOA) fraction formed by
oxidation of gaseous precursors, represented the largest part of OA. Amongst
these studies, the field deployment of the AMS in Marseille, the largest port
in the Mediterranean, has demonstrated that this instrument is well suited
for quantifying the contribution of industrial emissions (El Haddad et al.,
2013). In that work, the industrial OA factor was identified by the high
correlation with heavy metals and AMS polycyclic aromatic hydrocarbons
(AMS-PAHs); moreover strong increments of the industrial factor
concentrations were systematically observed when winds shifted to the
west–southwest, consistent with back-trajectory analysis highlighting the
transport of industrial emissions from an industrial pole. Overall the
industry-related OA contributed on average 7 % of the bulk OA mass
(El Haddad et al., 2011, 2013). However, these results were limited to
2 weeks of measurements during summer while the contribution of industrial
emissions during the rest of the year remains unknown.

There is a general paucity of AMS and aerosol chemical speciation monitor
(ACSM) datasets in the Mediterranean region during winter. Exceptions include
AMS campaigns (Mohr et al., 2012; Hildebrandt et al., 2011) covering a few
weeks during late winter–early spring and studies with an ACSM (e.g.,
Minguillón et al., 2015). The measurement
of organic markers and elements (e.g., Salameh et al., 2015; Reche et al.,
2012) at different stations indicate a substantial contribution from biomass
burning (BB). However, the sources and chemical composition of this fraction
and its evolution during the year remain uncertain. Modeling results within
the European Monitoring and Evaluation Programme (EMEP) have shown that the
south of France, together with Portugal, can be a major hotspot in Europe for
OA during February–March, possibly due to agricultural fires (Denier van der
Gon et al., 2015; Fountoukis et al., 2014). In this region, biomass burning
OA (BBOA) can derive from various processes such as agricultural land
clearing activities, wildfires, and domestic heating and therefore may have a
variable chemical composition.

The current study capitalizes on the AMS measurements of offline samples
collected over 1 year (2011–2012) in Marseille, an ideal environment for
the characterization of urban emissions from biomass burning, traffic, and
industrial activities and their transformation under high photochemical
activity. The source apportionment results obtained from PMF applied to the
OA mass spectra are corroborated using a comprehensive set of offline
measurements including elemental and organic carbon (EC / OC)
measurements, as well as measurements of elements by inductively coupled
plasma mass spectrometry (ICP-MS), of molecular markers by gas chromatography
mass spectrometry (GC-MS) and ultra-performance liquid chromatography mass
spectrometry (UPLC-MS), and of major ions by ion chromatography (IC). We
mainly focus on the sources and trends of winter OA and therefore we
additionally analyzed an online-AMS dataset acquired at the same location
during the winter of the previous year. The comparison of online- and
offline-AMS data and organic marker concentrations enables an in-depth
characterization of OA sources in Marseille and in particular the
identification of the main processes by which biomass smoke is emitted and
transformed in this region.

MethodsSite description

Marseille is the second largest city in France with more than 1 million
inhabitants (2010). It hosts the largest harbor in France and in the
Mediterranean Sea. Many port-related industries, especially petrochemical
companies, are located in a big cluster. These facilities are situated about
40 km NW from the city and include steel facilities, coke production plants,
oil storing, refining plants, and several shipyards. The Marseille commercial
harbor is located in the vicinity of this industrial cluster and represents
the third-largest harbor of the world for crude oil storage and treatment.
During summer, typical wind patterns in the city of Marseille favor the
transport of polluted air masses from the industrial cluster to the city,
including the sea breeze and the light Mistral wind from the Rhône Valley. At
night, the land breeze may transport air masses from an agricultural valley
located east of the sampling site. A more detailed description of wind
patterns in Marseille can be found in Drobinski et al. (2007) and Flaounas et
al. (2009). The sampling location is classified as an urban background
station and is situated in the urban park Cinq Avenue in a traffic-free
zone near the city center (43∘18′20′′ N,
5∘23′40′′ E; 64 m a.s.l.).

This work discusses the offline-AMS analysis of 55 composite samples (created
from the batch of 216 PM2.5 filters collected) which were analyzed by
Salameh et al. (2017) for major ions, molecular markers and elements
(Table S1 in the Supplement). A thorough description of the offline-AMS
analysis can be found in Daellenbach et al. (2016). One punch per filter
sample (from 5 to 25 mm diameter depending on the filter loading and on the
number of punches per composite sample) was prepared for analysis. Punches
from the same composite sample were extracted together in 15 mL of ultrapure
water (18.2 MΩ cm, total organic carbon < 5 ppb,
25 ∘C) in an ultrasonic bath for 20 min at 30 ∘C. After
extraction, filters were vortexed for 1 min, and the resulting liquids were
filtered with 0.45 µm nylon membrane syringe filters.

The generated liquid extracts were atomized in air using a custom-made
two-nozzle nebulizer. The generated aerosol was dried using a silica gel
diffusion drier and then measured by a high-resolution time-of-flight AMS
(HR-ToF-AMS, running in V-mode). In the AMS, particles are flash vaporized
(600 ∘C) and the resulting gas is then ionized by electron impact
(70 eV), yielding quantitative mass spectra of the non-refractory
submicron aerosol components, including OA, NO3-, SO42-,
NH4+, and Cl-. A detailed description of the AMS operating
principles, calibration protocols, and analysis procedures are provided by
DeCarlo et al. (2006). In total about 10 mass spectra (mass range
12–300 Da, 60 s averaging time) were collected per composite sample.
Between each sample, a measurement blank was recorded via nebulization of
ultra-pure water to minimize and monitor the possible memory effects of the
system. In total five mass spectra were collected per each measurement blank.
Offline-AMS data were processed and analyzed using the HR-ToF-AMS analysis
software SQUIRREL (Sequential Igor data Retrieval) v.1.52L and PIKA (Peak
Integration by Key Analysis) v.1.11L for IGOR Pro software package
(Wavemetrics, Inc., Portland, OR, USA). HR analysis of the mass spectra was
performed in the mass range 12–115 Da and in total 217 ion fragments were
fitted.

The interference of NH4NO3 on the CO2+ signal was corrected
according to Pieber et al. (2016) as follows:
CO2,real=CO2,meas-CO2,measNO3,measNH4NO3,pure⋅NO3,meas,
where the CO2,measNO3,measNH4NO3,pure correction factor was
2.5 % as determined from aqueous NH4NO3 measurements conducted
regularly during the measurement period.

Other offline measurements

A complete list of the measurements performed can be found in Table S1. To
summarize, major ions (Ca2+, Mg2+, K+, Na+, NH4+,
NO3-, SO42-, Cl-, oxalate, malate, succinate, and
malonate) were measured by IC according to the
methodology described by Jaffrezo et al. (1998). A subset of the filters was
selected for CO32- quantification following the method described by
Karanasiou et al. (2011). The method encompasses the fumigation of the filter
samples with HCl. The CO2 evolved by this acidification of the
carbonates deposited on the filters is detected by thermal-optical
transmittance determination. The CO32- measurements agreed fairly
well with the CO32- estimate from ion balance calculations based on
IC data (Fig. S1 in the Supplement). In the following discussion, ion
concentrations from filter samples always refer to the IC measurements unless
otherwise specified.

EC and OC were determined for each filter by
thermal-optical transmittance using a Sunset Lab analyzer (Birch and Cary,
1996) following the EUSAAR2 protocol (Cavalli et al., 2010). The
CO32- concentration determined from the IC ion balance was then
subtracted from OC concentration. The water-soluble OC (WSOC) was measured
with a total organic carbon analyzer (TOC) following the methodology
described in Bozzetti et al. (2016) and
references therein. Before the analyses, the liquid extracts were treated
with a 2 M HCl solution for 1–30 min to remove the inorganic C fraction.
Total nitrogen was determined using a TOC analyzer combustion tube. The
NO2 generated from the water-soluble N decomposition was detected
by a chemiluminescence TNM-1 unit detector. Organic markers were measured via
GC-MS analysis, following the methodology described in El Haddad et
al. (2009, 2011), Favez et al. (2010), and Piot et al. (2012). In total 15
different PAHs, 19 alkanes (C19–C36),
8 hopanes, 5 phthalate esters, levoglucosan, 6 lignin pyrolysis compounds,
6 fatty acids, and 3 sterols were determined (Table S1). Thirty-three chemical elements
(Table S1) were quantified using ICP-MS according to the procedure described
in Chauvel et al. (2010) and the modifications suggested in El Haddad et
al. (2011). A subset of 20 composite samples was selected for the
quantification of methyl-nitrocatechol isomers (Table S1) via ultra-performance liquid chromatography coupled with an electrospray ionization ToF-MS (UPLC-ESI-ToF-MS), following the procedure described in Iinuma et
al. (2010).

Intensive winter campaign

A HR-ToF-AMS was deployed at the same station (urban park Cinq Avenue)
between 25 January 2011 and 2 March 2011 to monitor the real-time NR-PM1
aerosol chemical composition. Although February 2011 is not included in the
sampling period covered by offline-AMS, these online measurements
provide a good opportunity to compare the separation, relative contributions,
and winter seasonal trends of the OA sources retrieved by the offline- and
online-AMS source apportionment procedures. Summer offline-AMS results were
instead compared with online-AMS source apportionment results reported by
El Haddad et al. (2013). The AMS was operated with an averaging time of
8 min, and in total 5633 mass spectra were collected during the monitoring
period. We performed an ionization efficiency (IE) calibration by
NH4NO3 nebulization, and the resulting IE value of 1.76×10-7 was applied to the dataset. The standard relative ionization (RIE)
efficiency was assumed for organics (1.4), SO42- (1.2), NH4+
(4), and Cl- (1.3), while the collection efficiency (CE) was estimated
using the composition-dependent collection efficiency model (Middlebrook et
al., 2012). Total AMS-PAHs were estimated from AMS data according to Dzepina
et al. (2007).

Similarly to offline-AMS, online-AMS data were also processed and analyzed
using HR-ToF-AMS Analysis software SQUIRREL
v.1.52L and PIKA v.1.11L for IGOR Pro
software package (Wavemetrics, Inc., Portland, OR, USA). HR analysis of the
mass spectra was performed in the mass range 12–115 Da and in total 215 ion
fragments were fitted.

A NOx analyzer was run in parallel to the AMS to monitor the real-time
NOx concentration. A set of pre-baked (500 ∘C for 3 h) 24 h
integrated PM2.5 filter samples was also collected during this campaign
(Batch 2) following the same sampling and storage procedure described in
Sect. 2.2. Filters were analyzed for major ions, metals, EC / OC, and organic markers, including n-alkanes,
hopanes, PAHs, and lignin and cellulose pyrolysis
products, using the techniques previously described in Sect. 2.2 (Table S1).

Source apportionmentImplementation

The online- and offline-AMS source apportionment results discussed in this
work were obtained from PMF analysis (Paatero and Tapper, 1994) of AMS
spectra using the Multilinear Engine (ME-2; Paatero, 1999). The Source Finder
toolkit (SoFi; Canonaco et al., 2013, v.5.1) for Igor Pro (Wavemetrics, Inc.,
Portland, OR, USA) served as interface for data input and result evaluation.
PMF is a multilinear statistical tool used to describe the variability of a
multivariate dataset as the linear combination of static factor profiles
times their corresponding time series, as described in Eq. (2):
xi,j=∑z=1pgi,z⋅fz,j+ei,j.
Here xi,j,gi,z,fz,j, and ei,j represent, respectively,
elements of the data matrix, factor time series matrix, factor profile
matrix,
and residual matrix, while subscripts i,j, and z denote time elements,
variables (in our case AMS fragments), and discrete factor numbers,
respectively. p represents the total number of factors selected by the user
for current given PMF solution. The PMF algorithm returns only gi,z and
fz,j values ≥ 0 and solves Eq. (2) by minimizing the object
function Q, defined as
Q=∑i∑jei,jsi,j2.
Here si,j is an element of the error input matrix. PMF is subject to
rotational ambiguity, i.e., different G⋅F
combinations characterized by the same Q can exist. The ME-2 implementation
of the PMF algorithm offers an efficient exploration of the solution space by
directing the solution toward environmentally meaningful rotations by
constraining the factor profile elements fz,j for one or more z
factors. In the a value implementation of ME-2, the elements of the factor
profile matrix F (in our case AMS fragments) are forced to
predefined values fz,j, allowing a certain variability defined by the
a value, such that the modeled element fz,j′ satisfies Eq. (4):
(1-a)fz,n(1+a)fz,m≤fz,n′fz,m′≤(1+a)fz,n(1-a)fz,m,
where n and m represent any two arbitrary variables in the normalized
F matrix. A complete description of the a-value approach can be
found elsewhere (Canonaco et al., 2013).

Monitoring periods.

Online-AMSOffline-AMS

28 Jan 2011–2 Mar 201130 Jul 2011–20 Jul 2012

For the offline-AMS source apportionment, the PMF input data matrix was
constructed as follows: each composite sample is represented by approximately
10 time points i, corresponding to the ∼ 10 mass spectra collected
per filter sample (Sect. 2.4). Each point of the data matrix is subtracted by
the average corresponding measurement blank.

The error matrices were instead constructed as follows. For online-AMS source
apportionment, the error matrix elements si,j were calculated according
to Allan et al. (2003) and Ulbrich et al. (2009) and included the
uncertainty deriving from electronic noise, ion-to-ion variability at the
detector, and ion counting statistics. si,j included also a minimum
error which was applied according to Ulbrich et al. (2009). For the
offline-AMS source apportionment, the error term δi,j was
calculated in the same way, but a further term (σi,j) including the
blank subtraction uncertainty was propagated according to Eq. (5):
si,j=δi,j2+σi,j2.
Finally for both online- and offline-AMS we applied a down-weighing factor
of 3 to all variables with an average signal to an average error ratio lower
than 2 (Ulbrich et al., 2009). No variable with an average signal to error
value lower than 0.2 was detected.

Dust and ash can contain significant amount of inorganic CO32-. Both
the IC balance and the CO32- measurements revealed non-negligible
contributions from CO32- in the PM2.5 fraction (Fig. S1).
Preliminary PMF results also resolved a factor correlating with Ca2+
(Supplement), which was characterized by high fCO2+, suggesting a
possible solubilization of CO32- from dust which could affect the OA
mass spectral fingerprint. Overall, as discussed in the Supplement, we could
not achieve a clear inorganic dust separation using PMF, and thus we opted
for a correction of the PMF input matrices. The measured pH of our filter
extracts was never > 8, and therefore we can exclude the presence of
CO32- in the extracts and assume all solubilized CO32- to
exist as HCO3-. Direct measurements of nebulized standard NaHCO3
aqueous solutions revealed that thermal decomposition of HCO3- on the
AMS vaporizer (600 ∘C) releases CO2 (Fig. S2). Currently no
HCO3- correction for the OA spectra is implemented in the standard
AMS fragmentation table (Aiken et al., 2008); therefore the measured
CO2+ signal needs to be subtracted from the OA AMS spectra.
Offline-AMS PMF input matrices were corrected for HCO3- and rescaled
for WSOMi (= WSOCTOC⋅ (OM : OC)offline-AMS)i according to the procedure described in the Supplement.

Online-AMS source apportionment optimization

In the following we describe the optimization of the online-AMS source
apportionment results. In order to optimize the source separation we
performed sensitivity analyses on PMF solutions. We adopted different
optimization strategies for online- and offline-AMS source apportionments
(Supplement) as we encountered dissimilar mixing between sources. This is
not surprising as the two methods are characterized by different time
resolution and different monitoring time extension (1 year for offline-AMS,
1 month for online-AMS), which in turn results in different variabilities
apportioned by the PMF algorithm (daily for online-AMS vs. seasonal for
offline-AMS).
In order to optimize the source separation, we performed sensitivity
analyses on PMF solutions according to the following scheme:
i.

Both the HOA and COA factors profiles are constrained by adopting an a-value
approach. An a-value sensitivity analysis was performed (121 PMF runs performed scanning all
the COA and HOA a-value combinations, a-value scanning steps: 0.1).

iv.

The 121 PMF runs are classified based on the cluster analysis of the COA
diurnal cycles; the best clusters, and corresponding PMF
solutions, are selected.

For online-AMS we selected a four-factor solution based on residual analysis.
We investigated the time-dependent Q(t)/Qexp(t) evolution when
increasing the number of factors. Q/Qexp is defined as the ratio
between Q (as defined in Eq. 3) and the remaining degrees of freedom of the
model solution (Qexp) calculated as i⋅j-(j+i)p (Canonaco
et al., 2013). A decrease of the Q/Qexp, from lower- to
higher-order solutions indicates an improvement in the variation explained by
the model. In particular we calculated the Δ(Q/Qexp(t))
obtained as the difference between the Q/Qexp(t) for a
factor solution minus the
Q/Qexp(t) value obtained from the (z-1)-factor solutions, where
z indicates the number of factors. We observed a large reduction of Δ(Q/Qexp(t)) until four factors (Fig. S4). Higher-order solutions
provided only minor contributions to the explained variability and, in terms
of solution interpretability, resulted in a splitting of primary sources
which could not be unambiguously associated with specific aerosol sources or
processes.

Using an a-value approach, we constrained HOA and COA profiles from Mohr et
al. (2012) and Crippa et al. (2013), respectively. Leaving COA and/or HOA
unconstrained enabled resolving COA only by increasing the number of factors
(> five-factor solutions) while in the four-factor solutions we observed a
splitting of an OOA factor which could not be attributed to specific
processes. Unconstrained PMF yielded HOA and COA time series correlated well
with the constrained solutions; however in the unconstrained case, HOA and
COA factor profiles showed higher fCO2+ in comparison to
literature studies (Crippa et al., 2013; Mohr et al., 2009, 2012; Bruns et al.,
2015; Docherty et al., 2011; Setyan et al., 2012; He et al., 2010) and in
comparison to the constrained PMF runs. This in turn resulted in higher HOA
and COA concentrations, with background night concentrations 2–3 times
higher than in the constrained solutions, possibly indicative of mixing with
oxidized aerosols (Fig. S5). Similar differences between constrained and
unconstrained PMF runs were also observed in Elser et al. (2016). Also the
HOA : NOx ratio (µg m-3/µg m-3)
matched typical literature values reported for France (0.02; Favez et al.,
2010) in the constrained PMF case (0.023), while for the unconstrained
approach it showed higher values (0.033).

For both offline- and online-AMS the constrained HOA profiles were from Mohr
et al. (2012), while the COA profiles were from Crippa et al. (2013). The
HOA profile from Mohr et al. (2012) was selected for offline-AMS consistently
with Daellenbach et al. (2016), since the same factor recovery distributions
were applied in this work. The same profile was applied to online-AMS for
consistency. Overall, as discussed in the Supplement, the HOA profiles from
literature showed high cosine similarities with each other, indicating that
the AMS mass spectral fingerprints from traffic exhaust are relatively stable
from station to station and consistent also with direct emission studies,
making the selection of the constrained factor profiles not crucial. More
variability instead is observed among COA literature profiles. For COA we
selected the profile from Crippa et al. (2013), which showed the lowest
fC2H4O2+ value among the considered ambient literature
spectra (Crippa et al., 2013; Mohr et al., 2012). This guaranteed a better
separation of COA from BBOA, as C2H4O2+ is strongly related
to levoglucosan fragmentation (Alfarra et al., 2007).

An a-value sensitivity analysis was performed by scanning all possible
a-value combinations for HOA and COA given by an a-value range 0–1 with
a step size of 0.1. In order to optimize the source apportionment results, we
retained only the PMF solutions satisfying an acceptance criterion described
hereafter.

PMF factors were associated with specific aerosol emissions/processes based on
mass spectral features, diurnal cycles, and time series correlations with
tracers. The identified factors were associated with traffic (HOA), cooking
(COA), biomass burning (BBOA), and OOA. A
thorough interpretation of the PMF factors will be discussed in Sect. 3.1. Given the absence of widely accepted tracers for COA emissions, the
optimization of the COA contributions was based on the analysis of the COA
diurnal cycles. From the HOA and COA a-value sensitivity analysis we
obtained a set of 121 PMF solutions, each one including both factor profiles
and factor time series. PMF solutions obtained in this way were categorized
according to a cluster analysis of the normalized COA diurnal cycles (Elser
et al., 2016, and references therein). The k-means clustering approach
enables classifying the PMF solutions into k clusters by minimizing a cost
function (C):
C=Σi,z((xi-μz,i)2),
where C represents the sum of the Euclidian distances between each
observation (xi) and its respective cluster center (μzi),
according to Eq. (6).

The number of clusters (k) that best represents the data is a critical
choice in order to perform a proper cluster analysis. The addition of a
cluster (k+1) on one hand adds complexity to the solution but on the
other hand decreases the cost function. A typical strategy to select the
right number of clusters is to explicitly penalize the addition of new
clusters by using Bayesian information criteria. This approach consists in
adding a penalty term to Eq. (6) proportional to the number of clusters
(k):
C′=Σi,z((xi-μz,i)2)+k⋅ln⁡(D),
where D denotes the dimensionality of the clusters (24 in our case, as we
consider diurnal cycles with hourly time resolution). In this study the C′
function showed the minimum at five clusters (Fig. S6). The absence of convexity
properties (i.e., several local minima can exist and the solution strongly
depends on the initialization) represents a possible drawback of the
k-means algorithm; therefore 100 random initializations of the k-means
algorithm were conducted.

The best clusters were selected based on a novel statistical analysis of the
HOA, COA, and BBOA average cluster spectra (Supplement). Briefly, a cluster
was retained when the HOA, COA, and BBOA average cluster spectra were not
statistically different from the average reference HOA, COA, and BBOA spectra
from literature (Crippa et al., 2013; Mohr et al., 2009, 2012; Bruns et al., 2015;
Docherty et al., 2011; Setyan et al., 2012; He et al., 2010, Table S3). A
complete description of the best clusters selection is reported in the
Supplement (Figs. S6–S10). Overall, three clusters were retained and two were
rejected. Finally, we retained only the PMF solutions that were attributed to
the three best clusters in more than 95 % of the k-means random
initializations (Fig. S9).

In order to explore the rotational ambiguity of our PMF model we performed
200 PMF runs by initiating the PMF algorithm using different input matrices.
The 200 different input matrices were generated using a bootstrap approach
(Davison and Hinkley, 1997; Brown et al., 2015). In short, the bootstrap
approach creates new input matrices by randomly resampling mass spectra (i
elements) from the original input matrices. Note that some mass spectra are
resampled multiple times, while others are not represented at all. On average
we randomly resampled 63 ± 1 % of the original spectra per
bootstrap PMF run. Finally, each bootstrap PMF run was initiated by randomly
varying the HOA and COA a values using the a-value HOA;
a-value COA combinations previously selected as optimal
from the cluster analysis (Fig. S10). Only solutions showing a higher COA
diurnal correlation with the three selected clusters than with the two
rejected clusters were retained. In this way we rejected 3.7 % of the
solutions. In the following we present the average bootstrap solution. The
source apportionment uncertainty was calculated as the variability of the
retained bootstrap PMF runs.

Offline-AMS source apportionment optimization

In this section we discuss the optimization of the offline-AMS source
apportionment. The PMF input matrices included 217 ions and 538 time elements
deriving from about 10 AMS mass spectral repetitions collected for each of
the 54 composite samples.

In order to optimize the source separation, we performed sensitivity
analyses on PMF solutions according to the following scheme:
i.

We explored the PMF rotational ambiguity exploration by performing 1080 bootstrap (Davison and Hinkley,
1997; Brown et al., 2015) PMF runs while simultaneously varying
the COA and HOA a-value combinations. PMF solutions were retained based on
the correlation of the PMF factors with external tracers. The PMF solutions
retrieved from this step are relative to the water-soluble fraction. The
corresponding water-soluble OC factor concentrations were determined by
dividing the water-soluble OM factor concentrations (PMF output) by the
OM : OC ratio determined from the corresponding factor mass spectra.

iv.

Retained water-soluble OC PMF solutions from step (iii) were rescaled to the
total OC concentrations by applying factor recoveries. Factor recoveries were
fitted (using a priori information) to match total OC. Only PMF solutions
and factor recoveries fitting OC with yearly and seasonally homogenous
residuals were retained. The average of the retained PMF solutions
represented the average source apportionment results. The corresponding
standard deviation represented the source apportionment uncertainty.

Based on analysis of the PMF residuals, we selected a five-factor solution to
explain the variability of our dataset (Fig. S11). Similar to online-AMS, we
monitored the decrease in Q/Qexp when increasing the number of
factors (z). In this study, a large Q/Qexp decrease was
observed until five factors. We also observed a clear ΔQ/Qexp
structure removal until five factors, with higher-order solutions leading to
additional factors that were not attributable to specific aerosol
sources or processes. The five separated factors included HOA, COA, BBOA, OOA, and
industry-related OA (INDOA). The complete validation of the PMF factors will
be discussed in Sect. 3.2.

As already mentioned, the HOA and COA profiles were constrained using an
a-value approach. Consistently with online-AMS we constrained the profiles
according to Mohr et al. (2012) and Crippa et al. (2013), respectively.
Unconstrained PMF runs for offline-AMS did not resolve HOA and COA factors.
To explore the rotational ambiguity of our PMF model we performed
1080 bootstrapped PMF runs. In this case we performed a higher number of
bootstrap runs than online-AMS because the COA and HOA a-value combinations
could not be separately optimized because the offline-AMS method cannot
resolve diurnal patterns. Each PMF run was also initiated using different
input matrices. As previously mentioned the input matrices contained about 10
mass spectral repetitions per filter sample, and therefore the bootstrap
algorithm was implemented to randomly resample 54 filters samples, each one
with all the corresponding mass spectral repetitions. The final generated
matrices included 54 samples; note that some filter samples could be
resampled more times, while others were not resampled at all. On average
63 ± 5 % of the original samples were resampled. Finally, each of
the PMF runs was initiated by randomly varying the HOA and COA a values.
The optimal PMF solutions were selected based on six acceptance criteria
including

BBOA correlation with levoglucosan (R) significantly higher than the
correlation between COA and levoglucosan;

HOA correlation with NOx significantly higher than the correlation
between COA and NOx;

INDOA correlation with Se significantly higher than the correlation between
COA and Se.

Criteria 1–3 analyze the correlation between factor and marker time series.
The significance of a correlation was determined by calculating the Fisher
transformed correlation coefficient l (Garcia, 2011):
l=0.5⋅ln⁡1+R1-R=arctan⁡R,
where R is the Pearson correlation coefficient between factor and marker
time series. Subsequently we conducted a t test to verify the significance
(α=0.95) of the correlation:
t=R1-R2N-2.
Here, N represents the number of samples (54). For a confidence interval of
95 % the minimum significant correlation was R=0.23. For criteria
4–6, in order to evaluate whether HOA, BBOA, and INDOA correlated
significantly better than COA with their corresponding markers, we compared
the l values obtained between each factor and its corresponding tracer
(e.g., BBOA and levoglucosan) and between COA and the same tracer (e.g.,
levoglucosan), using a standard error on the l distribution of
1/N-3 (Zar, 1999).

In total, we retained 1.5 % of the PMF runs. The criteria that discarded
the largest number of solutions were the ones based on the COA (4–6) correlation
with tracers of other sources. This suggests that for this dataset the COA
separation from other sources was particularly difficult due to the absence
of data with high temporal resolution, which aid the separation of a distinct COA
diurnal cycle. Moreover, this separation is also complicated by the small COA
contribution estimated by both online- and offline-AMS source apportionments
(on average 0.4 µg m-3 as discussed in the following
sections). Furthermore, the relatively small COA factor recovery
(RCOA median 0.54) hampers the COA apportionment by offline-AMS.

The PMF performed on offline-AMS mass spectra returned water-soluble OA
factor concentrations, WSKOAi. To rescale the water-soluble OA
concentration to the total OA, KOAi, we used the factor recoveries
(Rk) reported by Daellenbach et al. (2016) for the HOA, COA, BBOA, and
OOA factors (RHOA, RCOA, RBBOA,
ROOA).
KOAi=WSKOAi/RKOA
This is the first offline-AMS study where an INDOA factor was identified.
Therefore, we determined the INDOA recovery (RINDOA) in this
study by performing a single parameter fit according to Eq. (11):

OCi=WSHOAiOMOCHOA⋅RHOA+WSCOAiOMOCCOA⋅RCOA+WSBBOAiOMOCBBOA⋅RBBOA+WSOOAiOMOCOOA⋅ROOA+WSINDOAiOMOCINDOA⋅RINDOA.
Five hundred different fits were performed for each of the retained PMF solutions.
Moreover each fit was initiated using different RKOA combinations
randomly selected from the RKOA combinations determined by
Daellenbach et al. (2016) and reported in Bozzetti et al. (2016). In order to account for possible WSOC and OC
systematic measurement biases, each fit was initiated by also perturbing the
OCi, WSKOAi/ (OM : OC)WSKOC, and
RKOA inputs, assuming for each parameter a possible bias of
5 %, corresponding to the WSOC and OC measurement accuracy (we note that
the sum of the WSKOCi/ (OM : OC)WSKOC terms equals
WSOCi, neglecting the PMF residuals). Finally the input OCi was
randomly perturbed within its measurement uncertainty assuming a normal
distribution of the errors. Among the performed fits we retained the recovery
combinations and factor time series associated with OCi unbiased
residuals (residual distribution centered on 0 within the first and third
quartiles) for all seasons together and for summer and winter separately
(Fig. S12). Accordingly, we retained 13 % of the solutions. All the
retained factor recovery combinations can be found at
10.5905/ethz-1007-75. The median INDOA recoveries were estimated as
0.69 (first quartile 0.65, third quartile 0.73; Fig. S13), while the retained
RKOA for the other sources were consistent within the quartiles
with the RKOA values reported by Daellenbach et al. (2016)
despite their input value being perturbed as described above. The variability
of the retained solutions is considered our best estimate of the source
apportionment uncertainty, which accounts for offline-AMS
repeatability, RKOA uncertainties,
model rotational uncertainty (explored bootstrapping the input matrices and
scanning the HOA and COA a values), and RKOA
uncertainties. Overall, for a generic factor KOA, we estimated the
corresponding average relative uncertainty as follows: we calculated the
campaign averages of the KOA concentrations for each of the v retained PMF
solutions (KOAv‾). The relative uncertainty of the
KOA concentration was calculated as the standard deviation of
KOAv‾ divided by its average.

Online-AMS: average PMF factor mass spectra.

We also explored a four-factor solution without constraining the COA profile.
In this case we performed 100 bootstrap PMF runs by randomly varying the HOA
a value. Results
revealed that the COA separation (in the
five-factor solution with COA constrained) affected the HOA separation more
than the other factors (BBOA, OOA, INDOA). Overall, when comparing the four-
and five-factor solutions (without and with COA constrained, respectively).
HOA showed not statistically different concentrations within our
estimated source apportionment uncertainty for 85 % of the samples, BBOA
and OOA for 96 %, and INDOA for 94 %. This is probably due to the
high similarity between COA and HOA spectra (Supplement), which are both
characterized by high contributions from hydrocarbons.

Figure 1 displays the stacked seasonal average concentrations of the
measured PM2.5 components (ions measured by IC, elements measured by
ICP-MS, EC by the EUSAAR method, and OM estimated as the sum of the
offline-AMS PMF factors). Higher concentrations were observed during winter
than in summer due to the enhanced contributions of NO3- and OM.
NO3- increased during winter and autumn due to NH4NO3
partitioning into the particle phase at low temperatures. OM
concentrations were higher during winter due to the strong BBOA
contributions.

Online-AMS: average diurnal cycles of PMF factors and corresponding
tracers.

Overall OM was the dominant PM2.5 component over the whole year,
highlighting the importance of studying its sources. OM represented 46 %
of the total mass with higher relative contributions during winter (51 %)
than in summer (37 %). SO42- represented the second-most-abundant
PM2.5 component, contributing on average 12 % of the mass. Among the
other components, EC contributed 9 % of the mass, NO3- 9 %
(13 %avg during winter and 3 %avg during summer),
NH4+ 8 %, the sum of the elements 7 % (3 % during winter
and 13 % during summer, possibly because of dust resuspension),
CO32- 6 %, and Ca2+ 2 %. K+, Cl-, Na+, and
Mg2+ individually did not exceed 1 % of the mass. In the following,
subscripts avg and med denote average and median values, respectively.

Offline-AMS: water-soluble average mass spectra.

Online-AMS source apportionment validation

PMF factors were associated with aerosol sources/processes based on mass
spectral features (Fig. 2), correlation with tracers (Fig. 3), and diurnal
cycles (Fig. 4). In the following all the reported times are UTC + 2 local times. The HOA correlated well with NOx (R=0.86), with
peaks during rush hours (centered on 08:00 and 19:00) and higher concentrations
during the first half of the campaign. The average HOA : NOx ratio
(µg m-3/µg m-3) was 0.023, consistent
with Favez et al. (2010). The COA diurnal variation showed two peaks at lunch
and dinner time (12:00 and 21:00), as
observed in other cities (Elser et al., 2016; Mohr et al., 2012). The BBOA
factor profile showed the highest fC2H4O2+ and
fC3H5O2+ contributions among the apportioned factors.
Previous studies (Alfarra et al., 2007) associated the high
fC2H4O2+ and fC3H5O2+ contributions
in BBOA AMS spectra to the fragmentation of anhydrous sugars from cellulose
pyrolysis. The BBOA time series correlated well with levoglucosan (R=0.74) and AMS-PAHs (R=0.88). Note that AMS-PAHs are not unique BBOA
tracers, but in general they derive from combustion sources (see Supplement
for the comparison between AMS-PAHs and GC-MS PAHs). In this specific dataset
they could partially derive from traffic, although from the AMS-PAHs
multilinear regression we estimated that 79 % of the AMS-PAHs are
related to BBOA and 21 % to HOA, indicating that BBOA dominates the PAH
emissions. The AMS-PAHs : HOA ratio was 0.0020, while the AMS-PAHs : BBOA
was 0.0028.

In general, industrial emissions can be an important source of PAHs at this
location as discussed in El Haddad et al. (2013). In presence of an
industrial contribution, the BBOA vs. AMS-PAHs correlation would decrease.
In this work the correlation between AMS-PAHs and the
C2H4O2+ fragment, typically related to levoglucosan
fragmentation (Alfarra et al., 2007), was high (R=0.87) and no AMS-PAHs
spike was observed without a simultaneous increase of
C2H4O2+ (Fig. S15). Moreover the industrial-related OA
factor resolved by El Haddad et al. (2013) was clearly associated with wind
directions from W–SW (225–270∘), while in this work wind directions
were oriented from W–SW only for 7 % of the monitoring time, furthermore
without being associated with any significant increase in the AMS-PAHs
concentration (Fig. S16), indicating the absence of clear industrial
episodes.

The BBOA diurnal cycle, similarly to AMS-PAHs, showed higher values at night
than during the day (Fig. 4). In addition, the BBOA highest concentrations
were detected at night and associated with slow wind speeds from the E–NE which
is consistent with the night land breeze direction. Moreover, strong
enhancements of the BBOA factor concentrations were perceived when the wind
direction shifted to the E–NE (typically around 18:00 during the monitoring
period), suggesting that BBOA could be transported from the valleys near to
Marseille (Fig. S18).

We calculated the biomass burning OC (BBOC) time series by dividing the BBOA concentrations by the
OM : OCBBOA ratio calculated from the average BBOA HR spectrum
(1.60). The average levoglucosan : BBOC ratio
[µg m-3/µg m-3] was 0.15, comparable to
other European studies (Zotter et al., 2014; Herich et al., 2014;
Minguillón et al., 2011).

The OOA profile showed the most oxidized mass spectral fingerprint with an
O : C ratio of 0.67 in comparison to the values of 0.35 retrieved for BBOA,
0.12 for COA, and 0.03 for HOA. The OOA time series correlated well with
the NH4+ time series (R=0.86), suggesting a probable secondary
origin of the OOA factor (Lanz et al., 2008). The OOA diurnal cycle was flat,
suggesting OOA to be representative of regionally transported oxygenated
aerosols, consistent with the conclusions of El Haddad et al. (2013).

Offline-AMS source apportionment validation

PMF factors from the offline-AMS dataset were related to aerosol
sources/processes based on mass spectral features (Fig. 5), seasonal trends,
and correlation with tracers (Fig. 6). A comparison of the online-AMS and
offline-AMS factor profiles is reported in the Supplement. In the following,
for a generic k factor, we calculated the corresponding KOCi time
series by dividing KOAi by the OM : OC ratio determined from the average
HR-AMS factor profile.

During summer, when biomass burning contributions to EC are low, HOA
correlated well with EC (R=0.76) and yielded an HOC : EC
(hydrocarbon-like OC = HOA / (OM : OC)HOA) ratio of
0.64, similar to other European studies (El Haddad et al., 2009, and
references therein). Over the whole year, the retained PMF solutions showed
an HOA correlation with NOx (R) spanning between 0.23 and 0.49. These
low correlations are comparable to the ones found by El Haddad et al. (2013)
at the same station by online-AMS. In this case, the relatively low HOA
correlation with NOx is probably due to the low RHOA (median
0.11) that, together with the low HOA concentration
(1.5 µg mavg-3, Sect. 4.1), results in small
water-soluble HOA concentrations, leading to an uncertain HOA apportionment.
This was already reported in previous offline-AMS studies (Daellenbach et
al., 2016; Bozzetti et al., 2017). Although the HOA variability could not be
well captured, the estimated HOA concentration was corroborated by the
average HOA / NOx
(0.02 µg m-3/µg m-3), which was found to
be consistent with El Haddad et al. (2013) for the same station and with
Favez et al. (2010) for an alpine location in France.

BBOA was identified from its mass spectral features, with the highest
fC2H4O2+ and fC3H5O2+ contributions
among the apportioned factors, consistent with the findings of Alfarra et
al. (2007). BBOA correlated well with biomass combustion tracers measured by
GC-MS, such as levoglucosan (R=0.76), acetosyringone (R=0.71), and
vanillic acid (R=0.84). The winter average levoglucosan : BBOC
[µg m-3/µg m-3] ratio was equal to 0.12,
consistent with other studies in Europe (Zotter et al., 2014; Herich et al.,
2014; Minguillón et al., 2011).

The fourth factor (INDOA) was related to industrial emissions due to the high
correlation with light alkanes (C19–C22, 0.77≤R≤0.86), Se
(R=0.54), Pb (R=0.44), and some PAHs such as pyrene (R=0.74),
fluoranthene (R=0.77), and phenanthrene (R=0.74). Among the measured PAHs,
pyrene, fluoranthene and phenanthrene showed the lowest correlations with
levoglucosan (Table S1, R=0.31, 0.29, and 0.27, respectively), suggesting
that these particular PAHs were overwhelmingly emitted by INDOA rather than
BBOA. We note that phenanthrene, pyrene, and fluoranthene together represent
9.6 %avg of the PAHs mass quantified by GC-MS, indicating
that in total PAHs are overwhelmingly emitted by BBOA. While Se is considered to be a
unique coal marker in the literature (Weitkamp et al., 2005; Park et al.,
2014), in Marseille this source is likely related to coke and steel
production facilities (El Haddad et al., 2011). The average INDOA OM : OC
(1.60) was intermediate between the OM : OC ratios of HOA (1.23) and COA
(1.28) and those of BBOA (1.85) and OOA (1.82). El Haddad et al. (2013)
resolved an industrial OA factor at the same station by online-AMS PMF. In
that work the authors suggested a probable contribution of OOA to
the resolved industrial factor, probably deriving from (photo)chemical aging
during the transport from the industrial facilities to the receptor site
occasionally accompanied by new particle formation processes within the
industrial plume (as observed by the increased ultrafine particle number
concentration associated with W–SW wind directions). Considering the average
wind speed from W–SW (0.8 km h-1), and the distance between the
receptor site and the Marseille commercial harbor (∼ 40 km), we
estimate an aging time of several hours, which could lead to a more oxidized
fingerprint in comparison to the fresh primary emissions (Huang et al.,
2014). Overall this factor explained the largest fraction of the variability
of S- and Cl-containing organic fragments such as C2HSO+,
CH2SO+, CH3Cl2+, CH4SO3+,
C3H3SO2+, and C7H16+.

The last factor was defined as OOA as it showed a highly oxygenated
fingerprint with the largest CO2+ fractional contributions
(fCO2+) among the apportioned factors (14 %, in comparison with
11 % for BBOA, 2 % for HOA, and 1 % for COA and INDOA). This
factor showed on average the largest contributions over the year. Overall,
the OOA : NH4+ ratio was 2.3avg, in line with the
values reported by Crippa et al. (2014) for 25 different European sites
(2.0avg; minimum value 0.3; maximum 7.3).

Previous offline-AMS (Bozzetti et al., 2016, 2017; Daellenbach et al., 2016)
and online-ACSM studies (e.g., Canonaco et al., 2015) conducted in
Switzerland and Lithuania reported the separation of two OOA factors
characterized by different seasonal trends and different
C2H3O+ : CO2+ ratios. In particular, the OOA factor
characterized by the highest C2H3O+ : CO2+ ratio
contributed mostly during summer and was linked to secondary OA from biogenic
emissions. Here we calculated a
(C2H3O+ : CO2+)OOA ratio by subtracting
the C2H3O+ and CO2+ contributions deriving from
primary sources, from the measured C2H3O+ and CO2+
(Canonaco et al., 2015): C2H3O+CO2+OOA,i=(C2H3O+meas,i-HOAi⋅fC2H3O+HOA-BBOAi⋅fC2H3O+BBOA-INDOAi⋅fC2H3O+INDOA-COAi⋅fC2H3O+COA)/(CO2+meas,i-HOAi⋅fCO2+HOA-BBOAi⋅fCO2+BBOA-INDOAi⋅fCO2+INDOA-COAi⋅fCO2+COA).
Overall, C2H3OOOA+ and
CO2+OOA did not show a clear seasonality (Fig. S19),
which hampered the separation of two OOA sources. Even though another OOA
factor was not separated, El Haddad et al. (2013) estimated for the same
location during summer a substantial contribution of secondary biogenic
aerosol using 14C measurements (no measurements conducted in other
seasons). As a consequence the OOA factor resolved in this work explains both
secondary biogenic and aged/secondary anthropogenic sources. The absence of a
clear increase in the (C2H3O+ : CO2+)OOA
ratio in Marseille during summer could be explained by the large emissions of
anthropogenic secondary OA (SOA) precursors during winter, leading to a
different (C2H3O+ : CO2+)OOA seasonality
in comparison with previous offline-AMS studies (Daellenbach et al., 2016;
Bozzetti et al., 2016), which were
conducted either at rural sites characterized by different types of
vegetation or in smaller urban areas. In general, several parameters affect
the biogenic SOA concentrations and their separation, e.g., intensity of the
biogenic precursor sources, air masses photochemical age, and NOx
concentrations. All those parameters were different in Marseille from
previous offline-AMS studies which were conducted in central and northern Europe.

In this study, we present one of the first OA source apportionments
conducted over an entire year in the Mediterranean region. This work also
represents the first comparison between HR online-AMS and HR
offline-AMS source apportionments conducted at the same location, although in
two different periods. Previous studies (Daellenbach et al., 2016) reported
a comparison between offline-AMS and online-ACSM results.

Although related to different years and size fractions (PM1 online-AMS,
PM2.5 offline-AMS), the offline-AMS source apportionment returned
average seasonal factor concentrations not statistically different to
online-AMS for both winter (Fig. 7) and summer (comparison with El Haddad et
al., 2013, Fig. 8). We note that the total OC concentration quantified by
online-AMS for PM1 and by the thermal–optical procedure used for the
offline-AMS source apportionment of PM2.5 was not different on a
seasonal scale considering our uncertainty, which includes time variability
and measurements uncertainties.

Both online- and offline-AMS source apportionment revealed that BBOA was the
largest OA source during winter. Offline-AMS source apportionment estimated
an average BBOA concentration during winter 2011–2012 of
5.2 µg mavg-3, representing 43 %avg
of the OA. Similarly, online-AMS source apportionment revealed a BBOA
concentration of 4.4 µg mavg-3 (corresponding to 42 % of OA) during
February 2011. During summer, the offline-AMS BBOA concentration dropped to
an average of 0.3 µg mavg-3, representing 5 % of
the OA. Not surprisingly, such low BBOA contributions were not resolved by
online-AMS source apportionment during summer (El Haddad et al., 2013). On
average the offline-AMS BBOA relative uncertainty was 9 %. As a
comparison, the online-AMS BBOA average relative uncertainty was 6 %.
Overall for both online- and offline-AMS, the BBOA contributions were the
least uncertain among the primary sources, possibly because of the high
loadings and the distinct seasonal and diurnal BBOA variability in comparison
with the other separated factors. A comparison between the offline- and
online-AMS source apportionment uncertainties can be carried out with the
caveat that the online-AMS source apportionment uncertainties estimated in
this work should be considered as a low estimate as they do not account for
the AMS mass error deriving mostly from CE, and particle transmission. This
source of uncertainty affects the total OA mass but not the relative
contribution of the factors. By contrast, the OA mass uncertainty was
accounted for in the offline-AMS source apportionment as the OA mass was
rescaled to external measurements (WSOC and OC), the uncertainty of which was
propagated in the final source apportionment error (Sect. 2.4).

On a yearly scale, the offline-AMS source apportionment revealed that OOA was
the largest OA source, with the highest relative contributions during summer
due to the reduced BBOA emissions. The OOA concentration during summer was
estimated from offline-AMS at 3.0 µg mavg-3,
corresponding to 55 % of the OA mass. El Haddad et al. (2013) also
reported OOA to be the dominant OA fraction during summer with a similar
average concentration of 2.9 µg m-3. During winter, the OOA
concentration was estimated by online-AMS to be
3.9 µg mavg-3 corresponding to 38 % of the OA, while the OOA relative
uncertainty was 4 %. As a comparison, the OOA relative uncertainty from
offline-AMS was 6 %avg. The offline-AMS source apportionment
revealed similar OOA concentrations during winter
(3.4 µg mavg-3 corresponding to
27 %avg of the OA). Even though during winter the OOA
concentration was higher than in summer, possibly due to partitioning and
to the shallower boundary layer, the relative contribution decreased because
of the strong BBOA contributions.

HOA is one of the most uncertain factors, with an average relative
uncertainty of 39 % estimated from offline-AMS and 10 % from
online-AMS analysis, where the larger uncertainty observed for offline-AMS
derives mostly from the low RHOA and from the lower time
resolution, which does not capture the traffic diurnal variability. On
average, the HOA concentration predicted by offline-AMS was
1.5 µg m-3, corresponding to 17 % of the OA. The
estimated HOA concentration by online-AMS during February 2011 was
1.6 µg mavg-3 (16 % of OA). These values are
higher than the ones of El Haddad et al. (2013), who estimated a traffic
contribution of 0.8 µg mavg-3 during July 2008.

The COA contributions were only minor (average of
0.3 µg m-3), representing on average 4 % of the OA mass
according to the offline-AMS source apportionment. The online-AMS winter
source apportionment returned similar concentrations with
0.4 µg mavg-3, equivalent to 4 %avg
of the OA. Overall, due to the low concentrations, the COA contributions were
uncertain in both source apportionments (6 % for online-AMS, 73 % for
offline-AMS). Similarly to HOA, the larger uncertainty observed for
offline-AMS was most possibly due to the low RCOA and the low
time resolution, which did not enable the COA separation based on the diurnal
variability. The summer COA contribution was not resolved from HOA by
El Haddad et al. (2013), possibly because the COA reference mass spectrum was
not constrained and because of the lack of HR data which typically aid the
separation of the two sources.

Finally, the INDOA factor concentration estimated from offline-AMS was on
average 2.1 µg m-3 during winter and
0.6 µg mavg-3 during summer, where this seasonal
trend was driven by a strong episode that occurred during early February. The
offline-AMS relative uncertainty was estimated as 17 %. As previously
discussed (Sect. 3.1), this factor was not separated by online-AMS analysis
(February 2011) because of the absence of clear events, which in the
offline-AMS dataset were observed only over a short period during
January–February 2012. An industrial factor was instead resolved by
El Haddad et al. (2013) during summer 2008, with an average concentration of
0.3 µg mavg-3. In that study, the industrial OA
factor was also characterized by a low background intercepted by 10-fold
spiking episodes.

From the sum of the offline-AMS factor concentrations we estimated the total
OM mass. Using this OM and the measured OC we calculated the OM : OC ratio
to be 1.40 on average. Specifically, during winter this ratio was 1.55, which
is consistent with the online-AMS values determined from the HR-AMS spectra
(median 1.52, first quartile 1.46, third quartile 1.59). The bulk
OM : OC variability was driven by the source variabilities. Indeed the
relative contribution of the most oxidized source (OOA) was higher during
summer (mostly due to the absence of BBOA), but also the relative
contributions of the less oxidized sources (such as HOA and COA) were higher
during summer mostly due to low BBOA contributions. The BBOA mass spectrum
instead was associated with intermediate OM : OC ratios comprised between
the values of COA and OOA, and therefore influenced less strongly the bulk
OM : OC ratio. Overall the combination of these effects led to a higher
bulk OM : OC during winter.

Correlation between the sum of nitrocatechols (Table S1) with
levoglucosan and BBOC.

Offline-AMS (February 2012) and online-AMS (February 2011) smoothed
time-dependent levoglucosan : BBOC ratios. We note that the
levoglucosan : BBOC comparison should not be considered on a day-to-day
basis, where the levoglucosan : BBOC ratio in the 2 different years can be
coincidentally equal or different, but rather on a monthly timescale where,
as discussed in the paper, we observed a statistically significant
(p=0.05) evolution of the levoglucosan : BBOC ratio which is similarly
captured by the two models.

Insights into the BBOA origin during winter

Methyl-nitrocatechols measurements showed high correlations with BBOA
(Fig. 9, R=0.95) and no correlation with OOA (R=0.06, offline-AMS source
apportionment). Similarly high correlations were already observed in other
studies (e.g., Poulain et al., 2011). This large correlation difference
suggests that the variability of the methyl-nitrocatechols is likely
explained by the BBOA source. However, methyl-nitrocatechols are secondary
compounds deriving from the nitration of catechols, which can be either
directly emitted by wood combustion (Schauer et al., 2001) or generated by
OH⚫ oxidation of cresols directly released by wood combustion
(Iinuma et al., 2010). m-cresol/NOx photooxidation experiments
(Iinuma et al., 2010) revealed a total contribution of all
methyl-nitrocatechol isomers to the catechol SOA of approximately 10 %.
Assuming methyl-nitrocatechols to be entirely apportioned to the BBOA factor,
we estimate a methyl-nitrocatechol–SOA contribution to BBOA on the order of
8 %, indicating that part of the BBOA factor is of secondary origin.
Previous studies (Atkinson and Arey, 2003) revealed an o-cresol lifetime
in the atmosphere of 2.4 min towards NO3 and 3.4 h towards OH (at
298 K, dark conditions). This would suggest that such fast SOA formation can
be better traced by the high-time-resolution online-AMS source apportionment
(8 min) than by the offline-AMS with 24 h time resolution, and in any case
only in the BB plume or in the vicinity of the emission source. Nevertheless
we did not observe statistically different ratios (within 1σ, error
calculated as the time variability) of OOA : NH4+
(1.5avg and 1.25avg for the offline-AMS and
online-AMS source apportionments, respectively), OOA : BBOA
(0.65avg and 0.89avg, respectively), and
levoglucosan : BBOC (0.12avg and 0.12avg,
respectively, Fig. 10) during winter, suggesting that despite the different
time resolutions, the online and offline methods provide a comparable
BBOA-SOA separation. Overall these findings suggest that rapid SOA formation
is not well captured by PMF and rapidly formed SOA compounds (such as
nitrocatechols) can be systematically attributed by PMF to factors commonly
considered as “primary” (BBOA in this case). Both the online- and offline-AMS
source apportionment revealed for the two different winter seasons a
comparable temporal evolution of the levoglucosan : BBOC ratio (Figs. 10
and 11). This ratio showed typical literature values for domestic wood
combustion in Europe during January and early February (0.05–0.2; Zotter et
al., 2014; Herich et al., 2014; Minguillón et al., 2011), while during
late autumn and March (Fig. 11) it increased up to 0.3, highlighting an
evolution of the BBOA chemical composition. A similar seasonal trend was
observed for the ratios of levoglucosan : vanillic acid, levoglucosan : syringic
acid, and levoglucosan : non-sea-salt K+ (nss-K+; calculated
according to Seinfeld and Pandis, 2006) ratios (Fig. 11). Although the online
dataset was limited to 1 month of measurements, the
levoglucosan : vanillic acid ratio also showed a statistically significant
increasing trend from early February to the beginning of March (confidence
interval of 95 %, Mann–Kendall test). These results suggest the
occurrence of different types of biomass combustions during low-temperature
winter days compared to late autumn and early spring: levoglucosan derives
from cellulose pyrolysis (> 300 ∘C), while vanillic and syringic
acids result from lignin combustion (Simoneit et al., 1998; Sullivan et al.,
2008). Different reactivities/volatilities of BBOA markers may complicate
this analysis. For this reason we discuss in the following the levoglucosan
stability and propose that the major driver of the observed seasonal trends
is the occurrence of different BBOA combustions.

Online- and offline-AMS time-dependent levoglucosan : BBOC,
levoglucosan : vanillic acid, levoglucosan : syringic acid, and
levoglucosan : K+ ratios. The plant wax concentrations were
determined from GC-MS measurements of alkanes with an odd number of carbons
(Li et al., 2010). As discussed in the main text the spike observed in late
autumn could be related to incomplete green waste combustion.

Previous studies revealed the levoglucosan reactivity toward OH⚫
radical oxidation (Hennigan et al., 2010) both in gas and aqueous phase
(Hoffmann et al., 2010). In the following we analyze the levoglucosan and
nss-K+ time series in order to investigate the possible effects of
levoglucosan chemical stability and different types of biomass combustions on
the seasonal evolution of the levoglucosan : nss-K+ ratio. During
summer nss-K+ derives mostly from dust, while levoglucosan is depleted
by both photochemistry (Hennigan et al., 2010) and low BBOA emissions. Not
surprisingly the levoglucosan : nss-K+ ratio showed lower average
values in summer (0.23) than in winter (3.14). During winter nss-K+ is
considered to be mostly emitted by BBOA, and consistently in our dataset it
shows a good correlation with BBOA tracers (R=0.66 with syringic acid).
Overall, the levoglucosan : nss-K+ ratio during the cold season
manifests a behavior that is opposite to the photochemical activity (with
temperature considered as a proxy) as it shows higher values during March and
late autumn (up to 7.11) and lower in January and February (minimum = 2.79;
Fig. 11) when temperature is lower and photochemistry is less intense. For
these reasons we relate the winter levoglucosan : nss-K+ variability
to different types of combustion rather than to a levoglucosan depletion due
to photochemistry. Furthermore we observed the highest levoglucosan
concentrations (late autumn) simultaneously with the highest relative
humidity (89 %) values, suggesting the depletion of levoglucosan by
OH⚫ radical oxidation in aqueous phase to be
insignificant
(Hoffmann et al., 2010).

A similar winter seasonal behavior was observed also for plant waxes. Plant
wax concentrations were estimated from high-molecular-weight n-alkanes
(C24–C35) according to the methodology described by Li et al. (2010). This
methodology is based on the observation that alkanes from epicuticular waxes
preferentially contain an odd number of carbon atoms (Aceves and Grimalt,
1993; Simoneit et al., 1991). This was observed for a large variety of plants
including broad leaf trees, conifers, palms, shrubs, grasses, and groundcover
(Hildemann et al., 1996, and references therein). Waxes showed the highest
concentrations during late autumn (up to 0.16 µg m-3) and in
May (up to 0.17 µg m-3), while the minima were observed
during winter (minimum 0.007 µg m-3). In general, high-molecular-weight n-alkanes are typically detected in atmospheric aerosol in
significant amounts during the growing season. In a similar way, Hildemann et
al. (1996) estimated the highest plant wax concentrations in April–May in
Los Angeles and Pasadena, where the climate is similar to Marseille. Similarly
we observed the highest concentrations during May. However, comparable plant
wax concentrations were observed also in late autumn during the period
characterized by the highest levoglucosan : lignin combustion tracers
(Fig. 11), suggesting a possible emission from open combustion of green
wastes.

Taken together the above observations suggest the occurrence of combustion of
cellulose-rich material during March and late autumn, compared to lignin-rich
biomass burning for residential heating during January. The combustion of
cellulose-rich material is possibly related to agricultural waste burning at
the beginning and at the end of the agricultural cycle. The occurrence of
emission of biomass plumes due to land clearing episodes during March has
already been reported in other parts of Europe (Ulevicius et al., 2016) and
has been previously modeled for southern France (Denier van der Gon et al.,
2015; Fountoukis et al., 2014).

In this study we related the evolution of the BBOA composition over the cold
season to the combustion of cellulose-rich and lignin-rich fuels,
considering that lignin end cellulose are contained in different ratios in
different biomass fuels. This designation should not be considered as an
oversimplification of the combustion processes or of the fuel complexity
but rather as a classification of the BBOA based on our observations
of increasing lignin pyrolysis products over cellulose pyrolysis products
during the coldest days.

We note that BBOA is described in our PMF models by only one factor which
therefore potentially represents a combination of several types of biomass
burning sources. Increasing the number of factors did not lead to an
unambiguous separation of different BBOA sources, but the comparison
with source-specific markers revealed a real BBOA composition evolution over
the winter season with higher cellulose to lignin combustion tracer ratios
observed during late autumn and early spring in comparison to
January/February. This hypothesis of at least two types of BB sources (one
linked to domestic heating, another to agricultural activities) is also
supported by the direct PMF analysis of the organic and inorganic markers
measured for Batch 1 (Salameh et al., 2017).

Conclusions

PM2.5 filter samples were collected during an entire year (August 2011
to July 2012) at an urban site in Marseille, France. Filter samples were
analyzed by water extraction followed by nebulization of the liquid extracts
and subsequent measurement of the generated aerosol with an HR-ToF-AMS
(Daellenbach et al., 2016).

PMF analysis was conducted on the offline-AMS mass spectra and on online-AMS
data collected at the same station during February 2011. Offline-AMS source
apportionment results were also compared with a previous online-AMS source
apportionment study of 2 weeks during July 2008 at the same location
(El Haddad et al., 2013). The methods returned statistically similar seasonal
factor concentrations, although different years and size fractions were
considered (PM1 for online-AMS, PM2.5 for offline-AMS). OOA was the
major source of OA during summer representing on average 55 % of the OA
mass, while BBOA was the dominant OA source during winter contributing on
average 43 % of the OA. Smaller contributions were estimated for HOA,
INDOA, and COA, representing 17, 12, and 4 % of the OA mass, respectively.
The contribution of primary anthropogenic sources
(HOA + BBOA + COA + INDOA) was substantial over the year
(62 %avg of OA), with larger absolute and relative
contributions during winter (73 % of OAavg) associated with an
intense biomass burning activity.

Coupling offline- and online-AMS data with molecular markers showed
increasing levoglucosan : BBOC ratios during the late winter–early spring
period in both 2011 and 2012. This trend was also observed for the ratios
between cellulose and lignin combustion markers (e.g.,
levoglucosan : vanillic acid), with ratios approaching more typical
domestic wood combustion European values during January/early February, and
values characterized by higher values of cellulose-combustion markers during
late autumn and March indicative of the influence of different types of
fuels, possibly related to agricultural-related activities.

From the offline-AMS source apportionment, we observed a high BBOA
correlation with nitrocatechols deriving from the nitration of catechols
directly emitted by biomass combustion. These secondary components are
rapidly formed in the atmosphere in the presence of NO3⚫ (lifetime of a few minutes). Overall, despite the different time resolution,
online- and offline-AMS provided a comparable SOA–BBOA separation during
winter. Nevertheless, in case of fast SOA formation (relative to the timescale of the online-AMS time resolution or relative to the transport time to
the receptor site) this separation can be hindered, and further efforts are
needed to improve the SOA separation from BBOA.

All the retained factor recovery combinations can be found
at 10.5905/ethz-1007-75 (Bozzetti, 2017).

The Supplement related to this article is available online at https://doi.org/10.5194/acp-17-8247-2017-supplement.

Carlo Bozzetti thanks the Lithuanian–Swiss Cooperation Programme “Research
and Development” project AEROLIT (no. CH-3-.MM-01/08). Jay Gates Slowik
acknowledges the support of the Swiss National Science Foundation (starting
grant no. BSSGI0 155846). Imad El Haddad acknowledges the support of the
Swiss National Science Foundation (IZERZ0 142146).
María Cruz Minguillón acknowledges the Ramón y Cajal Fellowship awarded by the Spanish Ministry of Economy, Industry and Competitiveness.
This work has also been
supported by the MED program (APICE, grant number 2G-MED09-026:
http://www.apice-project.eu/), the French Environment and Energy
Management Agency (ADEME), and Provence-Alpes-Côte d'Azur (PACA) region.
Part of the OC / EC analysis carried out in MRS was supported by the
French national CARA program. This program is directed by Olivier Favez
(INERIS; http://www.ineris.fr/). He is gratefully
acknowledged. Edited by: Jason
Surratt Reviewed by: four anonymous referees